Ph.D. 2023
Joined Fall 2019Now: Computer Vision Research Scientist, Canon
- Ph.D., Mechanical Engineering, Iowa State University, 2023
- M.S., Mechanical Engineering, Shiraz University, 2018
- B.E., Mechanical Engineering, Shiraz University, 2015
Fateme joined the lab in Fall 2019. Her M.S. research was in control and vibration. Her main research interests included machine learning, deep learning, biomechanics, nonlinear control, robotics, mechatronics, and optimization. She is also interested in playing piano, reading, and mountain climbing.
She graduated from the SCS Lab in May 2023 and is currently a Computer Vision Research Scientist at Canon.
Research interests: Machine learning, deep learning, biomechanics, nonlinear control, robotics, mechatronics, optimization
Publications with the lab (10)
- Persistent monitoring of insect-pests on sticky traps through hierarchical transfer learning and slicing-aided hyper inference — Frontiers in Plant Science (2024)
- Dominating Set Model Aggregation for communication-efficient decentralized deep learning — Neural Networks (2024)
- Smart connected farms and networked farmers to improve crop production, sustainability and profitability — Frontiers in Agronomy 6 (2024)
Show all 10
- Cyber-agricultural systems for crop breeding and sustainable production — Trends in Plant Science 29 (2) (2024)
- Smart Connected Farms and Networked Farmers to Tackle Climate Challenges Impacting Agricultural Production — arXiv (2023)
- A modular vision language navigation and manipulation framework for long horizon compositional tasks in indoor environment — Frontiers in Robotics and AI (2022)
- Deep learning-based phenotyping for genome wide association studies of sudden death syndrome in soybean — Frontiers in plant science (2022)
- NAPPN Annual Conference Abstract: Genome wide association study of sudden death syndrome in soybean using deep learning-based phenotyping — Preprint (2022)
- How useful is active learning for image‐based plant phenotyping? — The Plant Phenome Journal (2021)
- Distributed deep learning for persistent monitoring of agricultural fields — NeurIPS 2021 AI for science workshop (2021)
fotouhif (@) iastate.eduGoogle Scholar